Considerable right skewness in precipitation distributions arises because of the relatively numerous occurrences of “no-rain” days [1]}. In fact, most precipitation values fall between 0 and 1, see Fig. REF .(a). In general, right skewness should be reduced to make patterns in the data more interpretable and helps to meet the assumptions of inferential statistics [2]}. For that, many transformations can be used such as log transformation, square root transformation and Box Cox[3]}. In our case, the “spike” of zeros all have the same value. Thus, no matter what transformation we apply, they are always going to have the same value. They are always going to be a “spike” sticking out from the rest of the distribution. Thus, we choose to apply log transformation to compress high values of precipitation [4]} and then to avoid that these values will be considered as noise by the neural network. In order to include zero values, we used \(log(x+1)\)  instead of \(log(x)\) . Finally, we standardize all variables to have a mean of zero and a standard deviation of 1 using z-score:
\(var_{standardized}=\frac{var-mean(var)}{stdev(var)},\) 
<FIGURE>